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Policy learning in continuous-time Markov decision processes using Gaussian Processes

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BRÁZDIL Tomáš BARTOCCI Ezio MILIOS Dimitrios SANGUINETTI Guido BORTOLUSSI Luca

Rok publikování 2017
Druh Článek v odborném periodiku
Časopis / Zdroj Performance Evaluation
Fakulta / Pracoviště MU

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Citace
www http://dx.doi.org/10.1016/j.peva.2017.08.007
Doi http://dx.doi.org/10.1016/j.peva.2017.08.007
Klíčová slova continuous-time Markov decision processes; reachability; gradient descent
Popis Continuous-time Markov decision processes provide a very powerful mathematical framework to solve policy-making problems in a wide range of applications, ranging from the control of populations to cyber–physical systems. The key problem to solve for these models is to efficiently compute an optimal policy to control the system in order to maximise the probability of satisfying a set of temporal logic specifications. Here we introduce a novel method based on statistical model checking and an unbiased estimation of a functional gradient in the space of possible policies. Our approach presents several advantages over the classical methods based on discretisation techniques, as it does not assume the a-priori knowledge of a model that can be replaced by a black-box, and does not suffer from state-space explosion. The use of a stochastic moment-based gradient ascent algorithm to guide our search considerably improves the efficiency of learning policies and accelerates the convergence using the momentum term. We demonstrate the strong performance of our approach on two examples of non-linear population models: an epidemiology model with no permanent recovery and a queuing system with non-deterministic choice.
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